A dynamic hierarchy resource management approach-DHRM based on intelligent prediction was proposed for heterogeneous cognitive network. In DHRM, according to different time scale, the method of wavelet neural network, wiener prediction and reinforcement learning were brought to get the variation of traffic distribution, the resource requirement of the handover calls, and the information of users' preferences, and available hierarchical resources of all networks were allocated flexibly. Multi-attribute decision making method, based on network status and user preference was used to make decision to dynamically assign network traffic flow to the most appropriate network. Simulation results show that, the system capacity is improved about 20% by DHRM compared with the other joint radio resource management algorithms.%针对异构认知网络中的资源管理问题,提出了基于认知的动态分级资源管理方法(DHRM).根据不同时间尺度,引入小波神经网络、基于维纳过程的预测方法和增强学习算法获得业务分布变化、切换呼叫资源需求量以及用户喜好等信息,从而动态调配异构多网络各级可用资源.在资源合理分配基础上,根据各网络实时状态以及用户喜好,通过多属性决策算法动态地将业务流分配到最佳接入网络中.仿真结果表明,DHRM相对于网间静态资源管理方法系统容量提高了约20％.
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